5 accepted papers
This study tackles robotic picking of multi-part deformable objects--common in warehouses yet underexplored in the literature--such as cable-attached appliances and pouch drinks, which comprise both rigid and deformable components. Their deformability poses a challenge to model-based 6D pose estimat…
This study tackles robotic picking of multi-part deformable objects—common in warehouses yet underexplored in the literature—such as cable-attached appliances and pouch drinks, which comprise both rigid and deformable components. Their deformability poses a challenge to model-based 6D pose estimator
Rather than traditional position control, impedance control is preferred to ensure the safe operation of industrial robots programmed from demonstrations. However, variable stiffness learning studies have focused on task performance rather than safety (or compliance). Thus, this paper proposes a nov…
An industrial connector insertion task requires submillimeter positioning and grasp pose compensation for a plug. Thus, highly accurate estimation of the relative pose between a plug and socket is fundamental for achieving the task. World models are promising technologies for visuomotor control beca…
State representation learning (SRL) in partially observable Markov decision processes has been studied to learn abstract features of data useful for robot control tasks. For SRL, acquiring domain-agnostic states is essential for achieving efficient imitation learning. Without these states, imitation…